Geodata stored in Geographic Information Systems (GIS) is becoming of increasing importance to many industries. In well-known consumer applications (such as Google™ and Bing™ maps), geodata specifies a respective location of a resource on a map. The geodata is typically useful for notifying persons where a resource (such as gas stations, restaurants, street number information, etc.) is located in a geographical region when creating a respective map. Local governments and public utilities now make extensive use of GIS systems to keep track of location information for different types of resources such as property lines, manhole covers, roads, businesses, etc.
GIS databases are typically searchable to find information of interest. In many instances, the GIS databases are populated with a certain amount of location information that is, to some degree or another, incorrect. As an example, geodata in a GIS database may indicate that a location of a restaurant is one location, where in reality, the restaurant is actually located at another location. Inaccurate geodata is undesirable for obvious reasons.
One possible reason for geodata inaccuracy is that GPS (Global Positioning System) data collected before the month of May in the year 2000 could be off by several tens of meters due to “selective availability”, that is, deliberate degradation of the civilian (L1) signal at the request of the U.S. DoD (Department of Defense).
Satellite positioning receivers have improved drastically over the years in terms of accuracy, particularly with the advent of WAAS (Wide Area Augmentation System) and hybrid receivers (for example the iPhone 4S) that simultaneously take advantage of the United States' GPS and Russia's GLONASS (GLObal NAvigation Satellite System). Another example of a position detection system is Galileo built by the European Union. “Newer” positional fixes with more modern equipment are, thus, likely to be more exact than older positional fixes.
Note that other conventional methods of position detection can include mobile network location triangulation, and/or WiFi™ access point location data, etc. Many conventional phones rely heavily on the latter both to improve speed of location resolution and to compensate for line of sight issues preventing a good GPS lock.
Currently, if a user of retrieved map information determines that a respective “marker” denoting the exact location of a given item in the geodata is not accurately placed on a displayed map, one way to correct such data is to manually identify the problem. For example, some web UIs (User Interfaces) provide a user the ability to input a text string such as “this symbol is incorrectly located” and drag the marker (representing a location of a respective landmark) to the more accurate latitude and longitude location on the map. Via the text string, the user can specify exactly the issue associated with the corresponding geodata. Typically, these text updates from users must be processed manually (reviewed by a human) because the corresponding data is not easily understood by a machine.